Machine learning-assisted pattern recognition and imaging of multiplexed cancer cells a porphyrin-embedded dendrimer array

• Published in: Journal of materials chemistry. B, Materials for biology and medicine Materials for biology and medicine, 2024-12, Vol.13 (1), p.27-217
• Main Authors: Hu, Jiabao, Ni, Weiwei, Han, Mengting, Zhan, Yunzhen, Li, Fei, Huang, Hui, Han, Jinsong
• Format: Article
• ISSN: 2050-750X; 2050-7518
• DOI: 10.1039/d4tb01861c

Early cancer detection plays a vital role in improving the survival rate of cancer patients, underscoring the importance of developing cancer detection methods. However, it is a great challenge to achieve simple, rapid, and accurate methods for simultaneously discerning various cancers. Herein we developed a 5-element porphyrin-embedded dendrimer-based sensor array, targeting the parallel discrimination of multiple cancers. The porphyrin-embedded dendrimers were modified with various functional groups to generate differentiated interactions with diverse cancer cells, which has been validated by fluorescence responses and laser confocal microscopy imaging. The dual-channel, five-element array, featuring ten signal outputs, achieved 100% accuracy in distinguishing between one human normal cell and six human cancerous cells, as well as in differentiating among mixed cells. Moreover, the screen 6-channel array can accurately distinguish 9 cells from mice and humans in minutes through optimization by multiple machine learning algorithms, including two normal cells and 7 cancerous cells with only 1000 cells, highlighting the significant potential of a porphyrin-embedded dendrimer-based parallel discriminating platform in early cancer diagnosis. The new 5-element porphyrin block copolymer sensor array allows simultaneous cell imaging and tumor type identification, offering promise for clinical applications.